Methods and devices for prediction of hypoglycemic events

ABSTRACT

Described herein are methods, devices, and microprocessors useful for predicting a hypoglycemic event in a subject. The hypoglycemic predictive approach described herein utilizes information obtained from a data stream, e.g., frequently obtained glucose values (current and/or predicted), body temperature, and/or skin conductance, to predict incipient hypoglycemic events and to alert the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Provisional Patent Application Ser.No. 60/226,431, filed 18 Aug. 2000, from which priority is claimed under35 USC §1 19(e)(1), and which application is incorporated herein byreference in its entirety.

TECHNICAL FIELD

Described herein are methods, devices, and microprocessors useful forpredicting a hypoglycemic event in a subject. The present invention forprediction of hypoglycemic events typically employs multiple parametersin the prediction. Such parameters include, but are not limited to,glucose readings (current and/or predicted), body temperature, and/orskin conductance.

BACKGROUND OF THE INVENTION

Hypoglycemia is the most critical acute complication of diabetes.Typically used present methods of self-monitoring of blood glucose(SMBG) provide periodic measurements of blood glucose obtained from afinger stick. This method produces measurements that, while veryaccurate, are too infrequent to detect hypoglycemic episodes.Frequently, in order to avoid hypoglycemia, diabetics maintainabnormally high blood glucose levels to provide a “buffer” against lowblood glucose levels. This constant high blood glucose level is the rootcause of most long-term complications of diabetes, namely, retinopathy,neuropathy, nephropathy, and cardiovascular disease. In effect, thepresent SMBG methods are forcing many diabetics to pay for a lower rateof acute complications with a higher rate of chronic complications inlater life.

The Diabetes Control and Complications Trial (DCCT) (The DiabetesControl and Complications Trial Research Group. New Engl. J. Med. 329,977-1036 (1993)) clearly showed that more blood glucose information isessential to better clinical outcomes. The subject group that measuredblood glucose and administered insulin more frequently (3-7 times perday) had a substantially lower rate of complications at the end of thestudy relative to the group that tested and injected less frequently.Even so, the tight control group was only able to reduce the averageblood glucose to a value approximately 50% above normal (153 mg/dL).Similarly, the HbA1c levels (a measure of average blood glucose levelover time) were lowered substantially relative to the control group, butnot into the normal range. As a result of this more intensive therapy,the tight control group experienced hypoglycemic events three times moreoften than the control group. These results demonstrate that three toseven blood glucose measurements per day are sufficient to lowerlonger-term complication rates, but still do not provide enoughinformation to bring average blood glucose levels to normal, or toprevent hypoglycemic events. Similar results have been obtained forsubjects on oral medication (UK Prospective Diabetes Study (UKPDS)Group, Lancet 352:837-853 (1998); Ohkubo Y, et al., Diabetes Research &Clinical Practice 28:103-17 (1995)), demonstrating the general benefitof frequent glucose monitoring in the management of diabetes. However,Bolinder, et al., (Diabetes Care 20:64-70 (1997)) show that even sevenmeasurements per day fail to detect more than one-third of allhypoglycemic events.

SUMMARY OF THE INVENTION

The present invention describes methods, devices, and microprocessorsfor predicting a hypoglycemic event in a subject. The methods of theinvention typically employ multiple parameters to be used in predictionof the hypoglycemic event. Such parameters include, but are not limitedto, current glucose readings (reflecting glucose amount or concentrationin the subject), one or more predicted future glucose reading, bodytemperature, and skin conductance.

In one aspect the present invention comprises a method for predicting ahypoglycemic event in a subject. The method comprises determiningthreshold values (or ranges of values) for the selected parameters,wherein the threshold values (or ranges of values) are indicative of ahypoglycemic event in the subject: e.g., determining (i) a thresholdglucose value (or range of values) that corresponds to the hypoglycemicevent, and (ii) at least one threshold parameter value that iscorrelated with the hypoglycemic event, wherein the parameter is eitherskin conductance readings or temperature readings. In one embodiment ofthe invention both skin conductance readings and temperature readingsare employed. A series of glucose measurement values is typicallyobtained at selected time intervals. In one embodiment the timeintervals are evenly spaced. Such a series may be obtained, for example,using a method comprising: extracting a sample comprising glucose fromthe subject using a transdermal sampling system that is in operativecontact with a skin or mucosal surface of the subject; obtaining a rawsignal from the extracted glucose, wherein the raw signal isspecifically related to glucose amount or concentration in the subject;correlating the raw signal with a glucose measurement value indicativeof the amount or concentration of glucose present in the subject at thetime of extraction; and repeating the extracting, obtaining, andcorrelating to provide a series of measurement values at selected timeintervals. In one embodiment, the sampling system is maintained inoperative contact with the skin or mucosal surface of the subject duringthe extracting, obtaining, and correlating to provide for frequentglucose measurements.

In the practice of this aspect of the method, either the current glucosevalue (time=n) or a measurement value predicted for a further timeinterval subsequent to the series of measurement values (e.g., time=n+1;that is, one time interval after the most recent measurement (time=n) inthe series of measurement values), is compared to the threshold glucosevalue, wherein a measurement value lower than or equal to the thresholdvalue is designated to be hypoglycemic.

A parameter value or trend of parameter values is measured concurrently,simultaneously, or sequentially with the obtaining of the series ofglucose measurement values. In one embodiment of the invention, theparameter value or trend of parameter values is reflective of eitherskin conductance readings or temperature readings of the subject. Theparameter value or trend of parameter values is compared with thethreshold parameter value (or range of values) to determine whether theparameter value or trend of parameter values indicates a hypoglycemicevent. A hypoglycemic event is predicted in the subject when both (i)comparing the predicted measurement value to the threshold glucose valueindicates a hypoglycemic event, and (ii) comparing one or more otherparameter (e.g., body temperature and/or skin conductance) with thethreshold parameter value (or range of values) indicates a hypoglycemicevent.

In one embodiment of the above method, the series of measurement valuescomprises three or more discrete values. In this embodiment, thepredicting of the measurement value at a further time interval may becarried out using the series of three or more measurement values in aseries function represented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$wherein y is the measurement value of glucose, n is the time intervalbetween measurement values, and α is a real number between 0 and 1. Theseries function may be used to predict the value of y_(n+1) where thetime interval n+1 occurs one time interval after the series ofmeasurement values is obtained.

When skin conductance is a selected parameter, the sampling systemtypically comprises a sweat probe and the skin conductance readings areobtained using the sweat probe.

When body temperature is a selected parameter, the sampling systemtypically comprises a temperature probe and the temperature readings areobtained using the temperature probe.

In one embodiment of the method of the present invention, the samplecomprising glucose is extracted from the subject into a collectionreservoir to obtain an amount or concentration of glucose in thereservoir. Such one or more collection reservoirs are typically incontact with the skin or mucosal surface of the subject and the sampleis extracted using an iontophoretic current applied to the skin ormucosal surface. Further, at least one collection reservoir may comprisean enzyme that reacts with the extracted glucose to produce anelectrochemically detectable signal, e.g., glucose oxidase.Alternatively, the series of glucose measurement values may be obtainedwith a different device, for example, using a near-IR spectrometer.

The present invention also includes a glucose monitoring system usefullfor performing the methods of the present invention. In one embodiment,the glucose monitoring system comprises, in operative combination, asensing mechanism (in operative contact with the subject or with aglucose-containing sample extracted from the subject, wherein thesensing mechanism obtains a raw signal specifically related to glucoseamount or concentration in the subject), a device to obtain either skinconductance readings or temperature readings from the subject, and oneor more microprocessors in operative communication with the sensingmechanism. The microprocessors comprise programming to (i) control thesensing mechanism to obtain a series of raw signals at selected timeintervals, (ii) correlate the raw signals with measurement valuesindicative of the amount or concentration of glucose present in thesubject to obtain a series of measurement values, (iii) when necessarypredict a measurement value at a further time interval, which occursafter the series of measurement values is obtained, (iv) compare thepredicted measurement value to a predetermined threshold value or rangeof values, wherein a predicted measurement value lower than thepredetermined threshold value is designated to be hypoglycemic, (v)control the device for measuring skin conductance readings ortemperature readings of the subject, (vi) compare the skin conductancereadings or temperature readings with a threshold parameter value, rangeof values, or trend of parameter values to determine whether the skinconductance readings or temperature readings indicate a hypoglycemicevent; and (vii) predict a hypoglycemic event in the subject when both(a) comparing the predicted measurement value to the threshold glucosevalue (or range of values) indicates a hypoglycemic event, and (b)comparing the skin conductance readings and/or temperature readings witha threshold parameter value, range of values, or trend of parametervalues indicates a hypoglycemic event.

The sensing mechanism of the monitoring system may, for example,comprise a biosensor having an electrochemical sensing element or anear-IR spectrometer. Further, the monitoring system may comprise adevice to obtain the skin conductance readings (e.g., a sweat probe)and/or a device to obtain the temperature readings (e.g., a temperatureprobe).

In one embodiment of the monitoring system, the predicting of ameasurement value at a further time interval is carried out using theseries of three or more measurement values in a series functionrepresented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$wherein y is the measurement value of glucose, n is the time intervalbetween measurement values, and a is a real number between 0 and 1.

In one aspect of the present invention, the method for prediction ofhypoglycemic events employs a decision tree that utilizes a hierarchicalevaluation of thresholds of selected parameters, where the thresholdsare indicative of a hypoglycemic event. Such parameters include, but arenot limited to, current glucose readings (reflecting glucose amount orconcentration in the subject), one or more predicted future glucosereading, body temperature, and skin conductance. In another aspect, thepresent invention comprises one or more microprocessors programmed tocontrol the above described methods, measurement cycle, devices,mechanisms, calculations, predictions, comparisons, evaluations, etc.The microprocessors may also mediate an alarm or alert related to thepredicted hypoglycemic event.

These and other embodiments of the present invention will readily occurto those of ordinary skill in the art in view of the disclosure herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 presents a schematic diagram of a skin-side view of theGlucoWatch® (Cygnus, Inc., Redwood City, Calif., US) biographer system.

FIG. 2 presents a comparison of GlucoWatch biographer measurement withconventional blood glucose measurement over 14 hours for one subject.

FIG. 3 presents data showing the average minimum temperature during eachGlucoWatch biographer measurement cycle vs. reference blood glucose.

FIG. 4 presents data showing average skin conductivity reading vs. bloodglucose range.

FIG. 5 presents data showing percentage of skin conductivity readingsindicating perspiration vs. blood glucose range.

DETAILED DESCRIPTION OF THE INVENTION

The practice of the present invention will employ, unless otherwiseindicated, conventional methods of diagnostics, chemistry, biochemistry,electrochemistry, statistics, and pharmacology, within the skill of theart in view of the teachings of the present specification. Suchconventional methods are explained fully in the literature.

All publications, patents and patent applications cited herein, whethersupra or infra, are hereby incorporated by reference in their entirety.

As used in this specification and the appended claims, the singularforms “a,” “an” and “the” include plural references unless the contentclearly dictates otherwise. Thus, for example, reference to “areservoir” includes a combination of two or more such reservoirs,reference to “an analyte” includes mixtures of analytes, and the like.

1. Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the invention pertains. Although other methods andmaterials similar, or equivalent, to those described herein can be usedin the practice of the present invention, the preferred materials andmethods are described herein.

In describing and claiming the present invention, the followingterminology will be used in accordance with the definitions set outbelow.

The term “microprocessor” refers to a computer processor contained on anintegrated circuit chip, such a processor may also include memory andassociated circuits. A microprocessor may further comprise programmedinstructions to execute or control selected functions, computationalmethods, switching, etc. Microprocessors and associated devices arecommercially available from a number of sources, including, but notlimited to, Cypress Semiconductor Corporation, San Jose, Calif.; IBMCorporation, White Plains, N.Y.; Applied Microsystems Corporation,Redmond, Wash.; Intel Corporation, Chandler, Ariz.; NEC Corporation, NewYork, N.Y.; and, National Semiconductor, Santa Clara, Calif.

The terms “analyte” and “target analyte” are used to denote anyphysiological analyte of interest that is a specific substance orcomponent that is being detected and/or measured in a chemical,physical, enzymatic, or optical analysis. A detectable signal (e.g., achemical signal or electrochemical signal) can be obtained, eitherdirectly or indirectly, from such an analyte or derivatives thereof.Furthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

A “sampling device,” “sampling mechanism” or “sampling system” refers toany device and/or associated method for obtaining a sample from abiological system for the purpose of determining the concentration of ananalyte of interest. Such “biological systems” include any biologicalsystem from which the analyte of interest can be extracted, including,but not limited to, blood, interstitial fluid, perspiration and tears.Further, a “biological system” includes both living and artificiallymaintained systems. The term “sampling” mechanism refers to extractionof a substance from the biological system, generally across a membranesuch as the stratum comeum or mucosal membranes, wherein said samplingis invasive, minimally invasive, semi-invasive or non-invasive. Themembrane can be natural or artificial, and can be of plant or animalnature, such as natural or artificial skin, blood vessel tissue,intestinal tissue, and the like. Typically, the sampling mechanism is inoperative contact with a “reservoir,” or “collection reservoir,” whereinthe sampling mechanism is used for extracting the analyte from thebiological system into the reservoir to obtain the analyte in thereservoir. Non-limiting examples of sampling techniques includeiontophoresis, sonophoresis (see, e.g., International Publication No. WO91/12772, published 5 Sep. 1991; U.S. Pat. No. 5,636,632), suction,electroporation, thermal poration, passive diffusion (see, e.g.;International Publication Nos.: WO 97/38126 (published 16 Oct. 1997); WO97/42888, WO 97/42886, WO 97/42885, and WO 97/42882 (all published 20Nov. 1997); and WO 97/43962 (published 27 Nov. 1997)), microfine(miniature) lances or cannulas, biolistic (e.g., using particlesaccelerated to high speeds), subcutaneous implants or insertions, andlaser devices (see, e.g., Jacques et al. (1978) J. Invest. Dermatology88:88-93; International Publication WO 99/44507, published Sep. 10, 1999International Publication WO 99/44638, published Sep. 10, 1999 andInternational Publication WO 99/40848, published Aug. 19, 1999).Iontophoretic sampling devices are described, for example, inInternational Publication No. WO 97/24059, published 10 Jul. 1997;European Patent Application EP 0942 278, published 15 Sep. 1999;International Publication No. WO 96/00110, published 4 Jan. 1996;International Publication No. WO 97/10499, published 2 Mar. 1997; U.S.Pat. Nos. 5,279,543; 5,362,307; 5,730,714; 5,771,890; 5,989,409;5,735,273; 5,827,183; 5,954,685 and 6,023,629, all of which are hereinincorporated by reference in their entireties. Further, a polymericmembrane may be used at, for example, the electrode surface to block orinhibit access of interfering species to the reactive surface of theelectrode.

The term “physiological fluid” refers to any desired fluid to besampled, and includes, but is not limited to, blood, cerebrospinalfluid, interstitial fluid, semen, sweat, saliva, urine and the like.

The term “artificial membrane” or “artificial surface,” refers to, forexample, a polymeric membrane, or an aggregation of cells of monolayerthickness or greater which are grown or cultured in vivo or in vitro,wherein said membrane or surface functions as a tissue of an organismbut is not actually derived, or excised, from a pre-existing source orhost.

A “monitoring system” or “analyte monitoring device” refer to a systemuseful for obtaining frequent measurements of a physiological analytepresent in a biological system. Such a device is useful, for example,for monitoring the amount or concentration of an analyte in a subject.Such a system may comprise, but is not limited to, a sampling mechanism,a sensing mechanism, and a microprocessor mechanism in operativecommunication with the sampling mechanism and the sensing mechanism.Such a device typically provides frequent measurement or determinationof analyte amount or concentration in the subject and provides an alertor alerts when levels of the analyte being monitored fall outside of apredetermined range. Such devices may comprise durable and consumable(or disposable) elements. The term “glucose monitoring device” refers toa device for monitoring the amount or concentration of glucose in asubject. Such a device typically provides a frequent measurement ordetermination of glucose amount or concentration in the subject andprovides an alert or alerts when glucose levels fall outside of apredetermined range. One such exemplary glucose monitoring device is theGlucoWatch biographer available from Cygnus, Inc., Redwood City, Calif.,US. The GlucoWatch biographer comprises two primary elements, a durableelement (comprising a watch-type housing, circuitry, display element,microprocessor element, electrical connector elements, and may furthercomprise a power supply) and a consumable, or disposable, element (e.g.,an AutoSensor component involved in sampling and signal detection, see,for example, WO 99/58190, published 18 Nov. 1999). This and similardevices is described, for example, in the following publications:Tamada, et al., (1999) JAMA 282:1839-1844; U.S. Pat. No. 5,771,890,issued 30 Jun. 1998; U.S. Pat. No. 5,735,273, issued 7 Apr. 1998; U.S.Pat. No. 5,827,183, issued 27 Oct. 1998; U.S. Pat. No. 5,954,685, issued21 Sep. 1999; U.S. Pat. No. 5,989,409, issued 23 Nov. 1999; U.S. Pat.No. 6,023,629, issued 8 Feb. 2000; EP Patent Application EP 0 942 278A2, published 15 Sept. 1999; PCT International Application WO 96/001100,published 4 Jan. 1996; PCT International Application WO 99/58190,published 18 Nov. 1999. The GlucoWatch biographer provides a device forfrequent sampling of glucose from a subject the application of lowintensity electric fields across the skin (iontophoresis) to enhance thetransport of glucose from body tissues to a sampling chamber. Inaddition, when the concentration or amount of glucose has beendetermined to be outside of a predetermined range of values theGlucoWatch biographer produces an alert or alarm signal. Such an alertor alarm is a component of the GlucoWatch biographer.

A “measurement cycle” typically comprises extraction of an analyte froma subject, using, for example, a sampling device, and sensing of theextracted analyte, for example, using a sensing device, to provide ameasured signal, for example, a measured signal response curve. Acomplete measurement cycle may comprise one or more sets of extractionand sensing.

The term “frequent measurement” refers to a series of two or moremeasurements obtained from a particular biological system, whichmeasurements are obtained using a single device maintained in operativecontact with the biological system over a time period in which a seriesof measurements (e.g, second, minute or hour intervals) is obtained. Theterm thus includes continual and continuous measurements.

The term “subject” encompasses any warm-blooded animal, particularlyincluding a member of the class Mammalia such as, without limitation,humans and nonhuman primates such as chimpanzees and other apes andmonkey species; farm animals such as cattle, sheep, pigs, goats andhorses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex and, thus, includes adultand newborn subjects, whether male or female.

The term “transdermal” includes both transdermal and transmucosaltechniques, i.e., extraction of a target analyte across skin, e.g.,stratum comeum, or mucosal tissue. Aspects of the invention which aredescribed herein in the context of “transdermal,” unless otherwisespecified, are meant to apply to both transdermal and transmucosaltechniques.

The term “transdermal extraction,” or “transdermally extracted” refersto any sampling method, which entails extracting and/or transporting ananalyte from beneath a tissue surface across skin or mucosal tissue. Theterm thus includes extraction of an analyte using, for example,iontophoresis (reverse iontophoresis), electroosmosis, sonophoresis,microdialysis, suction, and passive diffusion. These methods can, ofcourse, be coupled with application of skin penetration enhancers orskin permeability enhancing technique such as various substances orphysical methods such as tape stripping or pricking with micro-needles.The term “transdermally extracted” also encompasses extractiontechniques which employ thermal poration, laser microporation,electroporation, microfine lances, microfine cannulas, subcutaneousimplants or insertions, combinations thereof, and the like.

The term “iontophoresis” refers to a method for transporting substancesacross tissue by way of an application of electrical energy to thetissue. In conventional iontophoresis,;a reservoir is provided at thetissue surface to serve as a container of (or to provide containmentfor) material to be transported. lontophoresis can be carried out usingstandard methods known to those of skill in the art, for exampleby-establishing an electrical potential using a direct current (DC)between fixed anode and cathode “iontophoretic electrodes,” alternatinga direct current between anode and cathode iontophoretic electrodes, orusing a more complex waveform such as applying a current withalternating polarity (AP) between iontophoretic electrodes (so that eachelectrode is alternately an anode or a cathode). For example, see U.S.Pat. Nos. 5,771,890 and 6,023,629 and PCT Publication No. WO 96/00109,published 4 Jan. 1996.

The term “reverse iontophoresis” refers to the movement of a substancefrom a biological fluid across a membrane by way of an applied electricpotential or current. In reverse iontophoresis, a reservoir is providedat the tissue surface to receive the extracted material, as used in theGlucoWatch biographer glucose monitor (See, e.g., Tamada et al. (1999)JAMA 282:1839-1844; Cygnus, Inc., Redwood City, Calif.).“Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

The term “sensing device,” or “sensing mechanism,” encompasses anydevice that can be used to measure the concentration or amount of ananalyte, or derivative thereof, of interest. The sensing mechanism mayemploy any suitable sensing element to provide the raw signal (where theraw signal is specifically related to analyte amount or concentration)including, but not limited to, physical, chemical, electrochemical,photochemical, spectrophotometric, polarimetric, colorimetric,radiometric, or like elements, and combinations thereof. Examples ofelectrochemical devices include the Clark electrode system (see, e.g.,Updike, et al., (1967) Nature 214:986-988), and other amperometric,coulometric, or potentiometric electrochemical devices, as well as,optical methods, for example UV detection or infrared detection (e.g.,U. S. Pat. No. 5,747,806). Further examples include, a near-IR radiationdiffuse-reflection laser spectroscopy device (e.g, described in U.S.Pat. No. 5,267,152 to Yang, et al.). Similar near-IR spectrometricdevices are also described in U.S. Pat. No. 5,086,229 to Rosenthal, etal. and U.S. Pat. No. 4,975,581 to Robinson, et al. These near-IRdevices use traditional methods of reflective or transmissive nearinfrared (near-IR) analysis to measure absorbance at one or moreglucose-specific wavelengths, and can be contacted with the subject atan appropriate location, such as a finger-tip, skin fold, eyelid, orforearm surface to obtain the raw signal. In preferred embodiments ofthe invention, a biosensor is used which comprises an electrochemicalsensing element.

A “biosensor” or “biosensor device” includes, but is not limited to, a“sensor element” that includes, but is not limited to, a “biosensorelectrode” or “sensing electrode” or “working electrode” which refers tothe electrode that is monitored to determine the amount of electricalsignal at a point in time or over a given time period, which signal isthen correlated with the concentration of a chemical compound. Thesensing electrode comprises a reactive surface which converts theanalyte, or a derivative thereof, to electrical signal. The reactivesurface can be comprised of any electrically conductive material suchas, but not limited to, platinum-group metals (including, platinum,palladium, rhodium, ruthenium, osmium, and iridium), nickel, copper, andsilver, as well as, oxides, and dioxides, thereof, and combinations oralloys of the foregoing, which may include carbon as well. Somecatalytic materials, membranes, and fabrication technologies suitablefor the construction of amperometric biosensors are described by Newman,J. D., et al.(1995) Analytical Chemistry 67:4594-4599.

The “sensor element” can include components in addition to the sensingelectrode, for example, it can include a “reference electrode” and a“counter electrode.” The term “reference electrode” is used to mean anelectrode that provides a reference potential, e.g., a potential can beestablished between a reference electrode and a working electrode. Theterm “counter electrode” is used to mean an electrode in anelectrochemical circuit that acts as a current source or sink tocomplete the electrochemical circuit. Although it is not essential thata counter electrode be employed where a reference electrode is includedin the circuit and the electrode is capable of performing the functionof a counter electrode, it is preferred to have separate counter andreference electrodes because the reference potential provided by thereference electrode is most stable when it is at equilibrium. If thereference electrode is required to act further as a counter electrode,the current flowing through the reference electrode may disturb thisequilibrium. Consequently, separate electrodes functioning as counterand reference electrodes are preferred.

In one embodiment, the “counter electrode” of the “sensor element”comprises a “bimodal electrode.” The term “bimodal electrode” typicallyrefers to an electrode which is capable of functioningnon-simultaneously as, for example, both the counter electrode (of the“sensor element”) and the iontophoretic electrode (of the “samplingmechanism”) as described, for example, U.S. Pat. No. 5,954,685.

The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an ionically conductivematerial which contains an analyte or through which an analyte, or aderivative thereof, flows from a source thereof; (2) is comprised of acatalytic material (e.g., a platinum group metal, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (for example, hydrogenperoxide) into an electrical signal (e.g., an electrical current); and(4) defines the electrode surface area that, when composed of a reactivematerial, is sufficient to drive the electrochemical reaction at a ratesufficient to generate a detectable, reproducibly measurable, electricalsignal that is correlatable with the amount of analyte present in theelectrolyte.

An “ionically conductive material” refers to any material that providesionic conductivity, and through which electrochemically active speciescan diffuse. The ionically conductive material can be, for example, asolid, liquid, or semi-solid (e.g., in the form of a gel) material thatcontains an electrolyte, which can be composed primarily of water andions (e.g., sodium chloride), and generally comprises 50% or more waterby weight. The material can be in the form of a hydrogel, a sponge orpad (e.g., soaked with an electrolytic solution), or any other materialthat can contain an electrolyte and allow passage of electrochemicallyactive species, especially the analyte of interest. Some exemplaryhydrogel formulations are described in WO 97/02811, published Jan. 30,1997. The ionically conductive material may comprise a biocide. Forexample, during --manufacture of an AutoSensor assembly, one or morebiocides may be incorporated into the ionically conductive material.Biocides of interest include, but are not limited to, compounds such aschlorinated hydrocarbons; organometallics; hydrogen releasing compounds;metallic salts; organic sulfur compounds; phenolic compounds (including,but not limited to, a variety of Nipa Hardwicke Inc. liquidpreservatives registered under the trade names Nipastat®, Nipaguard®,Phenosept®, Phenonip®, Phenoxetol®, and Nipacide®); quaternary ammoniumcompounds; surfactants and other membrane-disrupting agents (including,but not limited to, undecylenic acid and its salts), combinationsthereof, and the like.

The term “buffer” refers to one or more components which are added to acomposition in order to adjust or maintain the pH of the composition.

The term “electrolyte” refers to a component of the ionically conductivemedium which allows an ionic current to flow within the medium. Thiscomponent of the ionically conductive medium can be one or more salts orbuffer components, but is not limited to these materials.

The term “collection reservoir” is used to describe any suitablecontainment method or device for containing a sample extracted from abiological system. For example, the collection reservoir can be areceptacle containing a material which is ionically conductive (e.g.,water with ions therein), or alternatively it can be a material, such asa sponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the shape of a disk or pad). Hydrogels are typicallyreferred to as “collection inserts.” Other suitable collectionreservoirs include, but are not limited to, tubes, vials, strips,capillary collection devices, cannulas, and miniaturized etched, ablatedor molded flow paths.

A “collection insert layer” is a layer of an assembly or laminatecomprising a collection reservoir (or collection insert) located, forexample, between a mask layer and a retaining layer.

A “laminate” refers to structures comprised of, at least, two bondedlayers. The layers may be bonded by welding or through the use ofadhesives. Examples of welding include, but are not limited to, thefollowing: ultrasonic welding, heat bonding, and inductively coupledlocalized heating followed by localized flow. Examples of commonadhesives include, but are not limited to, chemical compounds such as,cyanoacrylate adhesives, and epoxies, as well as adhesives having suchphysical attributes as, but not limited to, the following: pressuresensitive adhesives, thermoset adhesives, contact adhesives, and heatsensitive adhesives.

A “collection assembly” refers to structures comprised of severallayers, where the assembly includes at least one collection insertlayer, for example a hydrogel. An example of a collection assembly asreferred to in the present invention is a mask layer, collection insertlayer, and a retaining layer where the layers are held in appropriatefunctional relationship to each other but are not necessarily a laminate(i.e., the layers may not be bonded together. The layers may, forexample, be held together by interlocking geometry or friction).

The term “mask layer” refers to a component of a collection assemblythat is substantially planar and typically contacts both the biologicalsystem and the collection insert layer. See, for example, U.S. Pat. Nos.5,735,273, 5,827,183, and 6,201,979, all herein incorporated byreference.

The term “gel retaining layer” or “gel retainer” refers to a componentof a collection assembly that is substantially planar and typicallycontacts both the collection insert layer and the electrode assembly.

The term “support tray” typically refers to a rigid, substantiallyplanar platform and is used to support and/or align the electrodeassembly and the collection assembly. The support tray provides one wayof placing the electrode assembly and the collection assembly into thesampling system.

An “AutoSensor assembly” refers to a structure generally comprising amask layer, collection insert layer, a gel retaining layer, an electrodeassembly, and a support tray. The AutoSensor assembly may also includeliners where the layers are held in approximate, functional relationshipto each other. Exemplary collection assemblies and AutoSensor structuresare described, for example, in International Publication WO 99/58190,published 18 Nov. 1999; and U.S. Pat. Numbers 5,735,273 and 5,827,183.The mask and retaining layers are preferably composed of materials thatare substantially impermeable to the analyte (chemical signal) to bedetected; however, the material can be permeable to other substances. By“substantially impermeable” is meant that the material reduces oreliminates chemical signal transport (e.g., by diffusion). The materialcan allow for a low level of chemical signal transport, with the provisothat chemical signal passing through the material does not causesignificant edge effects at the sensing electrode.

The terms “about” or “approximately” when associated with a numericvalue refers to that numeric value plus or minus 10 units of measure(i.e. percent, grams, degrees or volts), preferably plus or minus 5units of measure, more preferably plus or minus 2 units of measure, mostpreferably plus or minus 1 unit of measure.

By the term “printed” is meant a substantially uniform deposition of anelectrode formulation onto one surface of a substrate (i.e., the basesupport). It will be appreciated by those skilled in the art that avariety of techniques may be used to effect substantially uniformdeposition of a material onto a substrate, e.g., Gravure-type printing,extrusion coating, screen coating, spraying, painting, electroplating,laminating, or the like.

The term “physiological effect” encompasses effects produced in thesubject that achieve the purpose of a therapy. In preferred embodiments,a physiological effect means that the symptoms of the subject beingtreated are prevented or alleviated. For example, a physiological effectwould be one that results in the prolongation of survival in a patient.“Parameter” refers to an arbitrary constant or variable so appearing ina mathematical expression that changing it gives various cases of thephenomenon represented (McGraw-Hill Dictionary of Scientific andTechnical Terms, S. P. Parker, ed., Fifth Edition, McGraw-Hill Inc.,1994). A parameter is any of a set of properties whose values determinethe characteristics or behavior of something. “Decay” refers to agradual reduction in the magnitude of a quantity, for example, a currentdetected using a sensor electrode where the current is correlated to theconcentration of a particular analyte and where the detected currentgradually reduces but the concentration of the analyte does not.

“Skip” or “skipped” signals refer to data that do not conform topredetermined criteria (for example, error-associated criteria asdescribed in U.S. Pat. No. 6,233,471, herein incorporated by reference).A skipped reading, signal, or measurement value typically has beenrejected (i.e., a “skip error” generated) as not being reliable or validbecause it does not conform with data integrity checks, for example,where a signal is subjected to a data screen which invalidates incorrectsignals based on a detected parameter indicative of a poor or incorrectsignal.

The term “Taylor Series Exponential Smoothing Function (“TSES”)”encompasses mathematical functions (algorithms) for predicting thebehavior of a variable at a different point in time, which factors inthe slope, and the rate of change of the slope. An example of a TSESfunction useful in connection with the present invention is a TSESfunction represented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$wherein: α is an optimizable variable which is a real number of between0 and 1, and is adjusted based on the particular measurements obtainedand the relationship between those measurements and actual results; n isan evenly spaced time interval;

and y is an analyte concentration or signal converted to an analyteconcentration 20 which signal measurement is optimized to fit theresults sought, e.g., to correspond with a reference analyteconcentration (see, for example, U.S. Pat. No. 6,272,364, issued 7 Aug.2001; WO 99 58973, published 18 Nov. 1999; both herein incorporated byreference in their entireties).

A “future time point” refers to the time point in the future at whichthe 25 concentration of the analyte of interest or another parametervalue is predicted. In preferred embodiments, this term refers to a timepoint that is one time interval ahead, where a time interval is theamount of time between sampling and sensing events.

2.0 Mides of Carrying out the Invention

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular formulationsor process parameters as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments of the invention only, and is notintended to be limiting.

Although a number of methods and materials similar or equivalent tothose described herein can be used in the practice of the presentinvention, the preferred materials and methods are described herein.

2.1 General Overview of the Invention

Hypoglycemia is the most important acute complication of diabetes and isa major obstacle to achieving optimal blood glucose control. Nocturnalhypoglycemia can be particularly troublesome for many patients. Theresearch proposed here utilizes information obtained from a data stream,e.g., frequently obtained glucose values, skin conductance or.temperature readings, generated by a frequent sampling glucosemonitoring device, e.g., the GlucoWatch biographer system, coupled witha time-series forecasting approach, to predict incipient hypoglycemicevents and to alert the user.

The invention is described herein with reference to the GlucoWatchbiographer system as an exemplary glucose monitoring system capable ofproviding frequent readings of glucose amount or concentration for auser. The GlucoWatch biographer system extracts glucose through the skinvia reverse iontophoresis and measures the extracted glucose with anamperometric biosensor. Glucose readings can be obtained, for example,every twenty minutes for a twelve-hour measurement period. Large-scaleclinical trials of this device in diabetic subjects have been completed(Tiemey, M. J., et al., Annals of Medicine, 32, 632-641 (2000); Tierney,M. J., et al., Diabetes Technology and Therapeutics, 2 (2), 197-205(2000); Tamada, J. A., et al., J. Am. Med. Assoc. 282, 1839-44 (1999)).

A major disadvantage of the current paradigm of discrete blood glucosemeasurements for self-monitoring of blood glucose (SMBG) levels fordiabetics is that the low number of measurements performed per day (onaverage 1.8 readings per day) is insufficient to track blood glucoseexcursions occurring between the measurements. More frequent monitoringis desirable both for determining the normal diurnal blood glucoseprofile, and for detection of hypoglycemic events. The GlucoWatchbiographer system measures glucose levels every 20 minutes, and has beenshown to track blood glucose levels accurately. In addition, theGlucoWatch biographer system sounds an audible alarm if the measuredglucose level falls below a user-settable low glucose threshold, or ifthe measured glucose level falls rapidly between successive readings.Although the present GlucoWatch biographer system is able to accuratelydetect the presence of hypoglycemic conditions, it is not currently ableto predict hypoglycemic events in advance.

Experiments performed in support of the present invention indicatemethods to improve the hypoglycemic event prediction ability of theGlucoWatch biographer system by combining (i) the continual stream ofglucose readings, with other physiological measures that are indicatorsof hypoglycemia, for example, (ii) skin temperature and/or (iii)perspiration. In a preferred embodiment, combinations of these threephysiological parameters results in a more robust predictor ofhypoglycemia.

In addition, the method of the present invention employs a time-seriesforecasting algorithm. This technique uses several previous readings topredict with sufficient accuracy the glucose level a short time in thefuture. Therefore, this technique could be used to predict incipienthypoglycemia. The time-series forecasting algorithm has been describedin co-owned, co-pending, WO 99/58973, published 18 Nov. 1999, hereinincorporated by reference in its entirety. Predictions based on thismethod are combined with predictions based on the methods describedabove.

Accordingly, one aspect of the present invention may be summarized asfollows. A series of conditional statements leading to a prediction of ahypoglycemic event are established. Such conditional statements may bebased on several processes. For example, a first process, e.g.,prediction of a hypoglycemic event related to information based oncurrent blood glucose values, and/or a second process, e.g., predictionof a hypoglycemic event related to a temperature-based prediction,and/or a third process, e.g., prediction of a hypoglycemic event relatedto a skin conductance-based prediction. A hypoglycemic event may bepredicted by any or all of these processes (or one process combining allof these processes). This information is then coupled with informationfrom, e.g., a fourth process, such as prediction of a hypoglycemic eventbased on a future value predicted by a time-series algorithm. Theinformation from several or all of these processes may then be evaluatedtogether. The more processes that predict a hypoglycemic event the morelikely that prediction of a hypoglycemic event is correct. Accordingly,combining the predictions of these processes results in a more robustpredictor of hypoglycemic events.

2.2 Description of an Exemplary Glucose Monitoring System

Numerous glucose monitoring systems can be used in the practice of thepresent invention. Typically, the monitoring system used to monitor thelevel of a selected glucose in a target system comprises a samplingdevice, which provides a sample comprising glucose, and a sensingdevice, which detects the amount or concentration of glucose or a signalassociated with the glucose amount or concentration in the sample.

An exemplary glucose monitoring system which provides frequentmeasurements of glucose amount or concentrations is the GlucoWatchbiographer system. This system is a wearable, non-invasive glucosemonitoring system that provides a glucose reading automatically everytwenty minutes. The GlucoWatch biographer system has several advantagesincluding, but not limited to, the fact that its non-invasive andnon-obtrusive nature encourages more frequent glucose testing amongpeople (or animals) with diabetes. Of greater clinical relevance is thefrequent nature of the information provided. Prior to the GlucoWatchbiographer system no method existed for frequent glucose measurementoutside of invasive means, often requiring hospital care (Mastrototaro,J. J., and Gross, T. M., “Clinical Results from the MiniMed ContinuousGlucose Monitoring System” Proc. 31^(st) Annual Oak Ridge Conference,April, 1999). The GlucoWatch biographer system provides more frequentmonitoring often desired by physicians in an automatic, non-invasive,and user-friendly manner. The automatic nature of the system also allowsmonitoring to continue even while a user is sleeping or otherwise unableto test.

The GlucoWatch biographer system comprises: (a) iontophoretic transportof glucose across the skin to non-invasively sample the glucose, (b) anelectrochemical biosensor to measure the glucose concentration, and (c)an intelligent data-processing algorithm that coverts the raw biosensorsignals to glucose readings while safeguarding against erroneous resultsthrough data point screening routines. These aspects of the system arebriefly described below and are described more extensively in thepublications referenced in the “Definitions” section, above.

The first aspect of the system is the iontophoretic extraction ofglucose. Many small molecules are transported through the skin by eitherpassive or facilitated means. Passive transport of compounds such asnicotine, estradiol, testosterone, etc. is the basis of transdermal drugdelivery (skin patches). Transport through human skin can be greatlyenhanced by the application of an electric field gradient. The use of alow-level electric current to enhance transport is known, generically,as iontophoresis.

Iontophoretic transport through skin can occur in either direction(Glikfeld, P., et al., Pharm. Res. 6, 988-990 (1989)). In particular, itwas shown that small molecules such as glucose, ethanol, andtheophylline are readily transported through the skin into an externalcollection chamber. Because transport through the skin is in theopposite direction to that used in iontophoretic drug delivery, thiseffect was described as “reverse iontophoresis” (U.S. Pat. No.5,362,307, issued Nov. 8, 1994.; U.S. Pat. No. 5,279,543, issued Jan.18, 1994.; U.S. Pat. No. 5,730,714, issued Mar. 24, 1998). In fact,because glucose is an uncharged molecule, transport is achieved throughelectro-osmosis. Results obtained from analyses using the GlucoWatchbiographer system showed that extracted glucose correlated closely withblood glucose (Tamada, J. A., et al., JAMA 282:1839-1844, 1999).

The second aspect of the system involves the use of an electrochemicalglucose biosensor. The GlucoWatch biographer system utilizes anelectrochemical biosensor assembly to quantitate the glucose extractedthrough the skin. There are two biosensors in the GlucoWatch biographersystem (FIG. 1). Each biosensor consists of a hydrogel pad containingthe enzyme glucose oxidase (GOx) and a set of electrodes. One surface ofthe hydrogel pad contacts the skin while the opposite surface is incontact with the biosensor and iontophoresis electrodes. The hydrogelpads serve two functions. During iontophoresis the pads serve as theelectrical contacts with the skin and the collection reservoirs for theextracted glucose. During the sensing portion of the cycle, the glucoseextracted through the skin reacts with the GOx in the hydrogel pads viathe reaction:

The H₂O₂ produced by this reaction is then detected amperometrically atthe platinum/carbon working electrode of the sensor. The integratedsensor current measured is proportional to-the concentration of H₂O₂,and ultimately to the amount of glucose extracted. The extraction andsensing portions of the cycle occur in succession, and the cycle repeatsto provide a measurement of glucose every twenty minutes.

For convenience to the user, the GlucoWatch biographer system wasdeveloped as a miniaturized device which can be worn on the wrist,forearm, upperarm, or other body part. The GlucoWatch biographer systemdurable component contains electronics for the biosensors andiontophoresis, a microprocessor, data storage memory, and an LCDdisplay. Two sets of biosensors and iontophoresis electrodes are fittedonto the skin side of the device (e.g., a consumable component, theAutoSensor). A schematic diagram of the AutoSensor of the GlucoWatchbiographer system is shown in FIG. 1.

Referring to FIG. 1, an exploded view of exemplary components comprisingone embodiment of an AutoSensor for use in an iontophoretic samplingsystem is presented. The AutoSensor components include twobiosensor/iontophoretic electrode assemblies, 104 and 106, each of whichhave an annular iontophoretic electrode, respectively indicated at 108and 110, which encircles a biosensor electrode 112 and 114. Theelectrode assemblies 104 and 106 are printed onto a polymeric substrate116 which is maintained within a sensor tray 118. A collection reservoirassembly 120 is arranged over the electrode assemblies, wherein thecollection reservoir assembly comprises two hydrogel inserts 122 and 124retained by a gel retaining layer 126 and mask layer 128. Furtherrelease liners may be included in the assembly, for example, a patientliner 130, and a plow-fold liner 132. In one embodiment, the electrodeassemblies comprise bimodal electrodes. A mask layer 128 (for example,as described in PCT Publication No. WO 97/10356, published 20 Mar. 1997,and U.S. Pat. Nos. 5,735,273, 5,827,183, 6,141,573, and 6,201,979, allherein incorporated by reference) may be present. Other AutoSensorembodiments are described in WO 99/58190, published 18 Nov. 1999, hereinincorporated by reference.

The mask and retaining layers are preferably composed of materials thatare substantially impermeable to the analyte (e.g., glucose) to bedetected (see, for example, U.S. Pat. Nos. 5,735,273, and 5,827,183,both herein incorporated by reference). By “substantially impermeable”is meant that the material reduces or eliminates analyte transport(e.g., by diffusion). The material can allow for a low level of analytetransport, with the proviso that the analyte that passes through thematerial does not cause significant edge effects at the sensingelectrode used in conjunction with the mask and retaining layers.Examples of materials that can be used to form the layers include, butare not limited to, polyester, polyester derivatives, otherpolyester-like materials, polyurethane, polyurethane derivatives andother polyurethane-like materials.

The components shown in exploded view in FIG. 1 are for use in aautomatic sampling system which is configured to be worn like anordinary wristwatch, as described, for example, in PCT Publication No.WO 96/00110, published 4 Jan. 1996, herein incorporated by reference.The wristwatch housing can further include suitable electronics (e.g.,one or more microprocessor(s), memory, display and other circuitcomponents) and power sources for operating the automatic samplingsystem. The one or more microprocessors may control a variety offunctions, including, but not limited to, control of a sampling device,a sensing device, aspects of the measurement cycle (for example, timingof sampling and sensing, and alternating polarity between electrodes),connectivity, computational methods, different aspects of datamanipulation (for example, acquisition, recording, recalling, comparing,and reporting), etc.

The third aspect of the system is an intelligent data-processingalgorithm that coverts the raw biosensor signals to glucose readingswhile safeguarding against erroneous results through data pointscreening routines. The raw current data obtained from the biosensorsmust be converted into an equivalent blood glucose value. Equations toperform this data conversion have been developed, optimized, andvalidated on a large data set consisting of GlucoWatch biographer andreference blood glucose readings from clinical trials on diabeticsubjects (see, for example, WO 018289A1, published 6 Apr. 2000). Thisdata conversion algorithm is programmed into a dedicated microprocessorin the GlucoWatch biographer system. The software also contains screensto exclude spurious data points that do not conform to objective, apriori criteria (e.g., data which contain noise above a certainthreshold). Exemplary signal processing applications include, but arenot limited to, those taught in the following U.S. Pat. Nos. 6,144,869,6,233,471, 6,180,416, herein incorporated by reference.

In addition to the two glucose biosensors, the GlucoWatch biographersystem also contains a temperature sensor and a skin conductivitysensor. Input from the former is used to exclude data points obtainedduring large thermal excursions. The skin conductivity input is used toexclude data obtained when the subject is perspiring profusely, as sweatcontains glucose which may confound the value obtained for the extractedsample. Hence, these various screens reject data points that may providefalse glucose information. The remaining data points are then suitablefor clinical use.

The GlucoWatch biographer system is housed in a plastic case held inplace, typically on the arm, with a wrist band. A single AAA battery isused as the primary power source with an additional back-up battery. TheGlucoWatch biographer circuitry includes a microprocessor and a customapplication specific integrated circuit (ASIC) chip containing thecircuitry to run both the iontophoresis and biosensor functions. Thereis sufficient memory to store up to 4000 glucose readings whichrepresents approximately three months of data with daily use. An LCDdisplay and four push buttons on the face of the GlucoWatch biographersystem comprise the user interface, and allow the user to control andcustomize the functions of the monitor as well as to display clock timeand date, glucose readings, and GlucoWatch biographer operation status.Data can also be downloaded to a PC via a serial interface adapter.

Included in the software control is the ability for the user to selecthigh and low glucose alert levels. If the GlucoWatch biographer systemmeasures a glucose value outside of these alert levels, an alarm soundsto notify the user of the situation.

The disposable portion of the GlucoWatch biographer system is theAutoSensor, which contains the two sets of biosensor and iontophoresiselectrodes and the corresponding hydrogel discs housed held in apre-aligned arrangement by a mask layer. The AutoSensor snaps into theskin-side of the GlucoWatch biographer system to make the necessaryelectrical connections between the two portions.

The GlucoWatch biographer system also contains a thermistor to measureskin temperature, and a set of conductivity probes which rest on thesurface of the skin to measure skin conductivity, a measure ofperspiration. As described above, the temperature and sweat data areused in the present device to ensure that the biosensor data has notbeen affected by large temperature excursions or perspiration during thereading period.

In another embodiment of a monitoring system, the sampling/sensingmechanism and user interface may be found on separate components (e.g.,WO 00/47109, published 17 Aug. 2000). Thus, the monitoring system cancomprise at least two components, in which a first component comprisessampling mechanism and sensing mechanism that are used to extract anddetect an analyte, for example, glucose, and a second component thatreceives the analyte data from the first component, conducts dataprocessing on the analyte data to determine an analyte concentration andthen displays the analyte concentration data. Typically, microprocessorfunctions (e.g., control of a sampling device, a sensing device, aspectsof the measurement cycle, computational methods, different aspects ofdata manipulation or recording, etc.) are found in both components.Alternatively, microprocessing components may be located in one or theother of the at least two components. The second component of themonitoring system can assume many forms, including, but not limited to,the following: a watch, a credit card-shaped device (e.g., a “smartcard” or “universal card” having a built-in microprocessor as describedfor example in U.S. Pat. No. 5,892,661, herein incorporated byreference), a pager-like device, cell phone-like device, or other suchdevice that communicates information to the user visually, audibly, orkinesthetically.

Further, additional components may be added to the system, for example,a third component comprising a display of analyte values or an alarmrelated to analyte concentration, may be employed. In certainembodiments, a delivery unit is included in the system. An exemplarydelivery unit is an insulin delivery unit. Insulin delivery units, bothimplantable and external, are known in the art and described, forexample, in U.S. Pat. Numbers 5,995,860; 5,112,614 and 5,062,841, hereinincorporated by reference. Preferably, when included as a component ofthe present invention, the delivery unit is in communication (e.g.,wire-like or wireless communication) with the extracting and/or sensingmechanism such that the sensing mechanism can control the insulin pumpand regulate delivery of a suitable amount of insulin to the subject.

Advantages of separating the first component (e.g., including thebiosensor and iontophoresis functions) from the second component (e.g.,including some microprocessor and display functions) include greaterflexibility, discretion, privacy and convenience to the user. Having asmall and lightweight measurement unit allows placement of the twocomponents of the system on a wider range of body sites, for example,the first component may be placed on the abdomen or upper arm. Thiswider range of placement options may improve the accuracy throughoptimal extraction site selection (e.g., torso rather than extremities)and greater temperature stability (e.g., via the insulating effects ofclothing). Thus, the collection and sensing assembly will be able to beplaced on a greater range of body sites. Similarly, a smaller and lessobtrusive microprocessor and display unit (the second component)provides a convenient and discrete system by which to monitor analytes.The biosensor readouts and control signals will be relayed via wire-likeor wireless technology between the collection and sensing assembly andthe display unit which could take the form of a small watch, a pager, ora credit card-sized device. This system also provides the ability torelay an alert message or signal during nighttime use, for example, to asite remote from the subject being monitored.

In one embodiment, the two components of the device can be in operativecommunication via a wire or cable-like connection. Operativecommunications between the components can be wireless link, i.e.provided by a “virtual cable,” for example, a telemetry link. Thiswireless link can be uni- or bi-directional between the two components.In the case of more than two components, links can be a combination ofwire-like and wireless.

2.3 Monitoring of Gulcose Levels

To evaluate the usefulness of the GlucoWatch biographer system in themonitoring of glucose levels, more than 90 subjects with diabetes wereenrolled at three clinical sites around the United States. Subjects worea GlucoWatch biographer system on their wrist for 15 hours while in aclinical setting. Subjects entered the clinic early in the morning in afasted state. The GlucoWatch biographer system was applied and a“warm-up” procedure of 175 minutes was initiated. At the end of thewarm-up period, the subjects took a single finger-stick blood glucosemeasurement which they used to calibrate the GlucoWatch biographerreadings. From that point on, the GlucoWatch biographer system tookthree measurements per hour for the remainder of the study. All datawere stored internally (i.e., in the biographer's memory). In addition,two standard blood measurements were obtained at 0 and 40 minutes duringeach hour. Thus, there were as many as 36 Gluco Watch biographer datapoints and 24 matching blood data points obtained from each subject.

The GlucoWatch biographer readings and blood data were then transferredinto a computer for algorithm development and subsequent data analysis.The data were randomly divided into two groups. The data from one partof the data set (46 GlucoWatch biographer systems) were used to “train”the algorithm (the Mixtures of Experts algorithm, see, for example, WO018289A1, published 6 Apr. 2000), that is, to determine the optimalfunctional form and parameter set needed to minimize the error betweenthe GlucoWatch biographer system-predicted glucose values and bloodglucose values. The optimized algorithm was then used to predict theGlucoWatch biographer system values for all subsequent data. This “outof sample” prediction technique diminished bias and demonstrated theuniversal nature of the algorithm. Data from one individual is shown inFIG. 2.

The result of this analysis for the 109 GlucoWatch biographer systems inthe “out of sample” test group showed a time-delay of about 15 minutesbetween the extracted glucose relative to the blood glucose. Using thepaired GlucoWatch biographer measurement-blood measurement data, anaverage correlation coefficient of 0.88 was obtained, and 97% of theresults fell in the clinically acceptable regions of the Clarke ErrorGrid Analysis (Clarke, W. L., et al., Diabetes Care 10:622-628 (1987)).In addition, the mean absolute error was 15.6%. Less than 8% of the datawere removed by the “temperature”, “sweat” and “noise” data integrityscreens. These and other statistical analyses suggested that theGlucoWatch biographer system is comparable to commercially availableblood monitoring devices over a broad range of values (40 to 400 mg/dLin these studies).

The clinical results cited above clearly demonstrate that the GlucoWatchbiographer system tracks glucose in human subjects with diabetes.

2.4 Temperature and Perspiration as Indicators of Hypoglycemia

Preliminary tests of the correlation between skin temperature and skinconductivity, and hypoglycemic blood glucose levels were performed ondata from one clinical trial. Temperature and perspiration data from theGlucoWatch biographer system were analyzed for a total of 213 GlucoWatchbiographer system applications on 121 diabetic subjects. This data setconsists of the temperature, perspiration measurement and referenceblood glucose value for 5346 GlucoWatch biographer measurement cycles.For this trial, the subjects were tested in a clinical setting, but wereallowed general freedoms simulating a home environment.

In order to determine whether a correlation existed between skintemperature and perspiration, and hypoglycemia, the data were sortedinto reference blood glucose range bins from <40 mg/dL to 240 mg/dL. Theminimum skin temperature for each measurement cycle in each bin wasaveraged and plotted in FIG. 3. As can be seen from the resultspresented in the figure, the skin temperature as measured by theGlucoWatch biographer system is lower than average when the referenceblood glucose is lower than 120 mg/dL, and is lowest when the bloodglucose is in the lowest hypoglycemic range. This preliminary resultdemonstrated a correlation between lower average skin temperature andhypoglycemic blood glucose levels.

Accordingly, in one aspect of the present invention, one of theparameters that may be used for the prediction of a hypoglycemic eventis a below average skin temperature. Ideally, an average skintemperature is determined for each subject by collecting a skintemperature reading data set over an extended period of time (e.g.,days, weeks, or months). An associated standard deviation and/or averagevariation may be associated with the average skin temperature usingstandard statistical methods applied to the skin temperature readingdata set. The average temperature may also be associated with the timeof day, for example, the day broken down into 1-8 hour increments(including all time values in the range, e.g., 2.5 hours) in order toaccount for normal skin temperature variations associated, for example,with a mid-day time period and a sleep time period. Such associationsmay be established employing standard statistical manipulations, such astrend analysis or multivariate analysis of variance. Further, usingtrend analysis or the TSES equation described herein, based on a seriesof skin temperature readings, a skin temperature reading at a futuretime point could be predicted or extrapolated. In one aspect of thepresent invention, the skin temperature reading parameter, when belowaverage body temperature for the subject, is an indicator of a possiblehypoglycemic event. As noted above, a standard deviation (and/orvariance) may be associated with the average body temperature of thesubject to provide a reference range. When the body temperature of thesubject falls below such a reference range (taking into accountstatistical variation, such as standard deviation), that is an indicatorof a possible hypoglycemic event. For example, for the cumulative datapresented in FIG. 3, such a reference range may be 31° C.±0.05° C. (ormore generally stated, average body temperature of the subjectplus/minus the standard deviation or variance associated with theaverage body temperature). Confidence intervals may also be used toestablish such ranges.

Similarly, if a decreasing body temperature trend is detected (forexample, using a regression analysis or other trend analysis) such atrend of decreasing body temperature may be used as an indicator of ahypoglycemic event.

In another aspect, fluctuations of body temperature may be used as anindicator of a hypoglycemic event: for example, such fluctuations may bedetermined relative to a reference range.

The data from the skin conductivity sensor on the GlucoWatch biographersystem was plotted in a similar manner. The GlucoWatch biographer skinconductivity measurement was converted to an arbitrary scale from 0-10.For data integrity screening purposes, skin conductivity readings above1 were considered an indication of perspiration occuring. FIG. 4 showsthe average skin conductivity reading for all the measurement cycleswithin each reference blood glucose range. The trend was relatively flatover the euglycemic and hyperglycemic ranges with the three highestaverages occuring in the <40 mg/dL, 40-59 mg/dL, and 60-79 mg/dL rangesin the hypoglycemic region, indicating a higher degree of perspirationin the hypoglycemic region.

The data shown in FIG. 4 was presented in a different manner by takingthe percentage of all readings with skin conductivity readings greaterthan one (therefore, above the a priori determined perspirationthreshold) and plotting them with reference to the same reference bloodglucose ranges (see FIG. 5). The data presented in FIG. 5 showedpronounced increase in the percentage of positive perspirationindications in the hypoglycemic regions below 60 mg/dL.

Accordingly, in one aspect of the present invention, one of theparameters that may be used for the prediction of a hypoglycemic eventis an above or below average sweat sensor reading (i.e., skinconductance). In one embodiment of the present invention, skinconductance above a predetermined perspiration threshold (or range) is apredictor of a hypoglycemic event (see, for example, reference data inFIGS. 4 and 5). Ideally an average skin conductance reading isdetermined for each subject by collecting a skin conductance readingdata set over an extended period of time (e.g., days, weeks, or months).An associated standard deviation and/or average variation may beassociated with the average skin conductance using standard statisticalmethods applied to the skin conductance reading data set. The averageskin conductance may also be associated with the time of day; forexample, the day broken down into 1-8 hour increments (including alltime values in the range, e.g., 2.5 hours) in order to account fornormal skin conductance variations associated, for example, with amid-day time interval and a sleep interval. Such associations may beestablished employing standard statistical manipulations, such as trendanalysis or multivariate analysis of variance. Further, using trendanalysis or the TSES equation described herein, based on a series ofskin conductance readings, a skin temperature reading at a future timepoint could be predicted or extrapolated. In one aspect of the presentinvention, the skin conductance reading parameter, when above or belowaverage skin conductance for the subject, is an indicator of a possiblehypoglycemic event. As noted above, a standard deviation (and/orvariance) may be associated with the average skin conductance of thesubject to provide a reference range. When the skin conductance of thesubject falls outside of such a reference range (taking into accountstatistical variation, such as standard deviation), that is an indicatorof a possible hypoglycemic event. For example, for the cumulative datapresented in FIG. 4, such a reference range may a skin conductancereading of 0.15±0.025 average sweat sensor reading (or more generallystated, average skin conductance of the subject plus/minus the standarddeviation or variance associated with the average skin conductance).Confidence intervals may also be used to establish such ranges.

Similarly, if an increasing or decreasing skin conductance trend isdetected (for example, using a regression analysis or other trendanalysis) such a trend of increasing or decreasing skin conductance maybe used as an indicator of a hypoglycemic event.

In another aspect, fluctuations of skin conductance may be used as anindicator of a hypoglycemic event: for example, such fluctuation can bedetermined relative to a reference range.

Body temperature (or body temperature trends) and/or skin conductance(or skin conductance trends) can be used together or singly asparameters useful for the prediction of a hypoglycemic event. Typically,use of such a parameter is coupled with the time series forecastingmethod described below.

Threshold values (or ranges of values) for selected parameters may beemployed in the prediction of hypoglycemic events. Such threshold valuescan be established, for example, based on review and analysis of arecord of the subject's glucose values, body temperature and skinconductance. A statistical program can be used to provide correlationsbetween known hypoglycemic events (from the subject's record, which iscreated using a glucose monitoring device capable of providing frequentglucose, temperature, and skin conductance readings) and the selectedparameters. Such statistical programs are known in the art and include,for example, decision tree and ROC analysis (see below).

2.5 Time Seried Forecasting

Time-series forecasting, the prediction of future values of a variablefrom past observations, is a procedure used for extrapolation of dataseries. There are a number of methods that may be used for time-seriesforecasting, -including, but not limited to, the following:extrapolation of linear or polynomial regression, autoregressive movingaverage, and exponential smoothing.

A method for time-series forecasting, called Taylor Series ExponentialSmoothing (TSES) has been developed and was disclosed in co-owned,co-pending WO 99/58973, published 18 Nov. 1999. In one embodiment, thismethod utilized the data points from the previous 60 minutes, as well asthe associated first and second derivative values to predict the valueof the next data point. The method of exponential smoothing calculatesthe predicted value of a variable y at time n+1 as a function of thatvariable at the current time n, as well as at two previous times n−1 andn−2. The equation that is typically used for the case of evenly spacedtime points is shown as equation (1) below.y _(n+1) =βy _(n)+β(1−β)y _(n−1)+β(1−β)² y _(n−2)   (1)In this equation, β is an empirical parameter obtained from experimentaldata which is typically between 0 and 1.

An improvement to equation (1) is as follows: First, there is aresemblance between equation 1 and a Taylor Series expansion, shown asequation (2). $\begin{matrix}{{f(x)} = {{f(a)} + {{f^{\prime}(a)}\left( {x - a} \right)} + \frac{{f^{''}(a)}\left( {x - a} \right)^{2}}{2!} + \ldots + \frac{{f^{({n - 1})}(a)}\left( {x - a} \right)^{({n - 1})}}{\left( {n - 1} \right)!}}} & (2)\end{matrix}$Accordingly, the variable Y_(n−1) was replaced by y′_(n) (the firstderivative at y_(n) with respect to time) and Y_(n−2) was replaced by$\frac{y_{n}^{''}}{2}$(the second derivative at y_(n) with respect to time) to give equation(3), $\begin{matrix}{y_{n + 1} = {{\beta\quad y_{n}} + {{\beta\left( {1 - \beta} \right)}y_{n}^{\prime}} + {\frac{{\beta\left( {1 - \beta} \right)}^{2}}{2}y_{n}^{''}}}} & (3)\end{matrix}$where the derivatives are calculated by the following two equations:$\begin{matrix}{y_{n}^{\prime} = \frac{y_{n} - y_{n - 1}}{\Delta\quad t}} & (4) \\{y_{n}^{''} = \frac{y_{n} - {2y_{n - 1}} + y_{n - 2}}{\Delta\quad t}} & (5)\end{matrix}$and Δt is the equally spaced time interval.

The analogy between equation (3) and the Taylor Series, equation (2),can be further improved by dividing the right hand side of equation (3)by β to give equation (6) where the definition α=1−β is used.$\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\quad y_{n}^{\prime}} + {\frac{\alpha^{2}}{2}y_{n}^{''}}}} & (6)\end{matrix}$Substituting equations (4) and (5) into equation (6), gives the finalexpression of the Taylor Series Exponential Smoothing (TSES) equationas: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$

The TSES equation is essentially an exponentially smoothed movingaverage Taylor series expansion using the first two terms of the Taylorseries. This technique may be adapted to work with the measurementsproduced by the GlucoWatch biographer system to predict glucose levelsat least one measurement cycle ahead (WO 99/58973, published 18 Nov.1999, herein incorporated by reference in its entirety).

2.6 Improved Prediction of Hypoglycemic Events

The present invention comprises methods for the improved ability topredict hypoglycemia which include a two-fold approach. First,additional physiological data, namely skin temperature and skinconductivity, are used in combination with frequent glucose valuereadings (obtained, for example, using the GlucoWatch biographer system)to produce a more robust prediction algorithm than may be achieved byusing any of the variables alone. Second, a time-series forecastingtechnique is used in conjunction with a data stream comprising frequentglucose measurements (obtained, for example, using the GlucoWatchbiographer system) to predict future glucose levels and provide an earlywarning of incipient hypoglycemic events. The synergy of these twodifferent approaches provides an improved ability to predicthypoglycemia events.

2.7 Incorporation of Sweat and Temperature Measurements into aHypoglycemia Prediction Algorithm

A data set consisting of approximately 16,000 pairs of GlucoWatchbiographer data and reference blood glucose values for approximately 450diabetic patients has been generated in support of the presentinvention. Both Type 1 and Type 2 diabetics with a wide variety ofdemographic backgrounds are represented in this data set. The data setmay be used as a test bed for developing and refining the incorporationof the skin temperature and conductivity readings into a hypoglycemiapredictive algorithm. The data set is sufficiently large to enable ahypoglycemia predictive algorithm to be trained on a randomized subsetof data, and tested on a separate “out of sample” subset. Using this setof raw data, GlucoWatch biographer system outputs can be produced usingan emulator program which completely mimics the device operation. Theskin temperature and conductivity readings are incorporated into ahypoglycemia alert function in the emulator, and the simulated results(glucose readings, occurrence of hypoglycemia alert soundings, etc.) arerecorded and predictive efficacy evaluated.

A number of different functions are evaluated for their ability tocorrectly predict hypoglycemia using the skin temperature, skinconductivity, and glucose data. The preliminary data presented in FIGS.3-5 and described above represent the simplest of these functions, thatis, use of the discrete data points at each GlucoWatch biographermeasurement cycle. More complex algorithms may utilize, for example,variation of the temperature and conductivity parameters from a slidingaverage baseline value, monitoring of trends in these parameters, ormore complex neural network approaches.

Numerous suitable estimation techniques useful in the practice of theinvention are known in the art. These techniques may be used to providecorrelation. factors (e.g., constants), which correlation factors arethen used in a mathematical transformation to obtain a measurement valueindicative of a hypoglycemic event. In particular embodiments, thehypoglycemic predictive algorithm may apply mathematical, statisticaland/or pattern recognition techniques to the problem of signalprocessing in chemical analyses, for example, using neural networks,genetic algorithm signal processing, linear regression, multiple-linearregression, principal components analysis of statistical (test)measurements, decision trees, or combinations thereof. The structure ofa particular neural network algorithm used in the practice of theinvention may vary widely; however, the network may, for example,contain an input layer, one or more hidden layers, and one output layer.Such networks can be trained on a test data set, and then applied to apopulation. There are many suitable network types, transfer fuinctions,training criteria, testing and application methods which will occur tothe ordinarily skilled artisan upon reading the instant specification.One such evaluation method is a Mixtures of Experts algorithm (see, forexample, WO 018289A1, published 6 Apr. 2000; U.S. Pat. No. 6,180,416,issued 30 Jan. 2001, herein incorporated by reference in its entirety).In a Mixtures of Experts algorithm, skin conductance and/or bodytemperatures can be included as parameters to provide more accurateprediction of blood glucose and, in particular, more accurate predictionof potential hypoglycemic events.

One method to evaluate the effectiveness of a proposed hypoglycemiaalert function examines each set of paired GlucoWatchbiographer/reference blood points to determine whether the hypoglycemiaalert function correctly predicted the presence or absence ofhypoglycemia. The number of false positives (prediction of hypoglycemiawhen none existed) or false negatives (missing hypoglycemia when it didexist) is tabulated and used to calculate the sensitivity andspecificity of the alert function.

A second analysis anticipates that each hypoglycemic episode can bepredicted by several readings in the continual data stream of theGlucoWatch biographer system. For such an analysis, the number ofhypoglycemic events predicted (and not predicted) by the hypoglycemiaalert function of GlucoWatch biographer system is tabulated and used tocalculate the predictive value of the hypoglycemia alert function. Usingsuch approaches the hypoglycemia alert function is optimized on apre-existing data set and is then tested in clinical trials on diabeticpatients.

Accordingly, the incorporation of data from the sweat and temperatureprobes into the glucose-level prediction algorithm is tested using theexisting clinical database. Optimization of the algorithm parameters isperformed to minimize error in the glucose readings and maximize theaccuracy of the hypoglycemia alarm function.

2.8 Time-Series Forecasting Algorithm

The GlucoWatch biographer system's ability to acquire glucose data on afrequent basis creates a large database heretofore unavailable to apatient or clinician. The time-series forecasting algorithm describedabove uses a series of closely spaced glucose readings to “forecast” afuture reading. This algorithm provides an early warning of incipienthypoglycemic events, the most serious acute complication for diabetics.

An adaptive neural network technology may be combined with this timeforecasting concept to provide a system that is customized to anindividual patient's physiology. This process involves training theneural network with a sufficient number of paired monitor and referenceblood glucose values from a given patient. In this way, the neuralnetwork “learns” the patterns in an individual's blood glucose changes.This approach reduces error in the prediction of hypoglycemia events.

Optimization of forecasting algorithms is carried out using the “datamining” approach essentially as described above to investigate the skintemperature-conductivity data. The time-series forecasting algorithmsare trained and tested on the data set of GlucoWatch biographer systemvalues and corresponding blood glucose reference values obtained duringclinical trials and described above. Various statistical measures ofaccuracy are used to evaluate and optimize forecasting algorithmsincluding difference statistics (mean error, mean relative error, meanabsolute error), RMS error, and the Clarke Error Grid Analysis. Theoptimized forecasting algorithm is then prospectively tested in clinicaltrials essentially as follows.

Initial clinical trials are conducted with non-diabetic volunteers inorder to verify that the modified GlucoWatch biographer systems functionproperly. Such trials also provide an early assessment of thecapabilities of the hypoglycemia alert function. The clinical protocolis essentially performed as follows. A 100 gram oral glucose tolerancetest (OGTT) has historically predicted device performance in apopulation of subjects with diabetes. In addition, following OGTT, afterthe glucose peak, non-diabetic subjects can achieve blood glucose levelsas low as 50-70 mg/dL from endogenous insulin production, thus providingdata to test the prediction of hypoglycemia. Moreover, since one subjectmay wear multiple GlucoWatch biographer systems, meaningful data may beobtained with as few as 10 subjects.

Following trials with non-diabetic subjects, the modified GlucoWatchbiographer system comprising an improved hypoglycemic alert function istested on subjects with diabetes. Typically, results from a minimum of20 subjects over at least five consecutive days are used to generatedata sufficient to develop and optimize the algorithms. The demographicprofile of the subjects included in these clinical trials is diverse, asit is beneficial to investigate performance on as wide a demographicsample as possible. These trials typically study subjects with both Type1 and Type 2 in relatively equal numbers. Male and female subjects arerepresented fairly evenly. The subject population has a wide range ofages. The ethnic background of a typical large clinical trial is shownbelow in Table I as an example where 120 of the subjects are female and111 were male. Typically the test population comprises subjects 18 yearsor older. TABLE 1 American Indian or Asian or Black, not of White, notof Alaskan Pacific Hispanic Hispanic Other or Native Islander OriginHispanic Origin Unknown Total 1 1 24 36 166 3 231

The general design of the study day is as follows. The subjects arriveat the clinic in the morning having fasted from midnight the nightbefore, and having not taken their morning insulin injection. TwoGlucoWatch biographer systems are applied to the subject's arm,synchronized with clock time, and started. Over the course of the study(approximately 15 hours), capillary blood samples are obtained twice perhour, and measured with a reference method for comparison with theGlucoWatch biographer measurements. During the course of the measurementperiod, insulin dosing is adjusted by the investigator to achieve mildlyhypoglycemic and hyperglycemic glucose levels. The targeted bloodglucose range is 40-450 mg/dL. At the end of the 15 hour study, theGlucoWatch biographer systems are removed by laboratory personnel.

The data collected from each patient consists of demographicinformation, medical screening data, reference blood glucosemeasurements, and GlucoWatch biographer system measurements. These dataare used for the purposes of evaluating the hypoglycemic predictionalgorithm.

Accordingly, the optimum time-series algorithm model and variables to beused in the model are determined by “training” and testing on a largedatabase of clinical GlucoWatch biographer system data. The algorithm isoptimized to minimize error in the glucose readings and maximize theaccuracy of the hypoglycemia alarm function. This optimized time-seriesprediction model is combined with one or more predictions ofhypoglycemic events, e.g., using a sweat and temperature probe basedpredictive algorithm, as described above. The hypoglycemic predictiveapproach described herein utilizes information obtained from a datastream, e.g., frequently obtained glucose values, skin conductance ortemperature readings, generated by a frequent sampling glucosemonitoring device, e.g., the GlucoWatch biographer system, coupled witha time-series forecasting approach, to predict incipient hypoglycemicevents and to alert the user.

One or more microprocessors may be used to coordinate the functions ofthe sampling device, sensing device, and predictive algorithms. Such amicroprocessor generally uses a series of program sequences to controlthe operations of the sampling device, which program sequences can bestored in the microprocessor's read only memory (ROM). Embedded software(firmware) controls activation of measurement and display operations,calibration of analyte readings, setting and display of high and lowanalyte value alarms, display and setting of time and date functions,alarm time, and display of stored readings. Sensor signals obtained fromthe sensor electrodes can be processed before storage and display by oneor more signal processing functions or algorithms which are stored inthe embedded software. The microprocessor can also include anelectronically erasable, programmable, read only memory (EEPROM) forstoring calibration parameters, user settings and all downloadablesequences. A serial communications port may be used to, for example,allow the monitoring device to communicate with associated electronics,for example, wherein the device is used in a feedback controlapplication to control a pump for delivery of a medicament such asinsulin (using, e.g., an insulin pump).

Accordingly, one aspect of the present invention provides a method forpredicting a hypoglycemic event in a subject. Typically, a thresholdglucose value or range of glucose values is determined that correspondsto a hypoglycemic event. Symptom producing low plasma glucose levelsvary in individuals and in different physiological states. Abnormallylow plasma glucose is usually defined as less than or equal to about 50mg/dL in men, about 45 mg/dL in women, and about 40 mg/dL in infants andchildren. The methods of the present invention for prediction of ahypoglycemic event are, generally, to avoid glucose levels dropping tosuch low levels in the subject. Accordingly, a threshold for a glucosemeasurement value indicative of a hypoglycemic event may be set higher(e.g., between about 80 to about 100 mg/dL) in order to give the subjectmore time to respond and prevent glucose levels from dropping into thehypoglycemic range. Further, at least one threshold parameter value (orrange of values) that is correlated with a hypoglycemic event is alsodetermined, for example where the parameter is skin conductance readingor body temperature reading.

A series of glucose measurement values at selected time intervals isobtained using a selected glucose sampling system (for example, theGlucoWatch biographer). Using the series of measurements, typically aseries of at least three glucose measurement values, a glucosemeasurement value at a further time interval (e.g., n+1, where the lastglucose measurement value of the series was n) subsequent to the seriesof measurement values is predicted. This predicted glucose measurementvalue can be obtained, for example, using the time series forecastingmethod described above. Other predictive algorithms may be used as well.

In addition, another parameter value or trend of parameter values ismeasured concurrently, simultaneously, or sequentially with theobtaining of the series of glucose measurement values. Skin conductanceand body temperature are two preferred parameters. Either the parametervalue (for example at time point n, or a predicted value for theparameter at a later time point, for example, n+1) or trend of parametervalues are compared with a threshold parameter value (or range ofvalues) to determine whether the measured parameter value or trend ofparameter values is suggestive of a hypoglycemic event. A hypoglycemicevent is predicted for the subject when both (i) comparing the predictedglucose measurement value to the threshold glucose value indicates ahypoglycemic event at time interval n+1, and (ii) comparing saidparameter with said threshold parameter value indicates a hypoglycemicat time interval n or n+1. Typically one or more microprocessors areprogrammed to control data acquisition (e.g., the glucose measurementcycle and obtaining of skin conductance and/or body temperaturereadings) by being programmed to control devices capable of collectingthe required data points. The one or more microprocessors also typicallycomprise programming for algorithms to control the various predictiveand comparative methods.

2.9 Prediction of Hypoglycemic Events Using a Decision Tree Model

In one aspect of the present invention, the method for prediction ofhypoglycemic events employs a decision tree (also called classificationtree) which utilizes a hierarchical evaluation of thresholds (see, forexample, J. J. Oliver, et. al, in Proceedings of the 5th AustralianJoint Conference on Artificial Intelligence, pages 361-367, A. Adams andL. Sterling, editors, World Scientific, Singapore, 1992; D. J. Hand, etal., Pattern Recognition, 31(5):641-650, 1998; J. J. Oliver and D. J.Hand, Journal of Classification, 13:281-297, 1996; W. Buntine,Statistics and Computing, 2:63-73, 1992; L. Breiman, et al.,“Classification and Regression Trees” Wadsworth, Belmont, Calif., 1984;C4.5: Programs for Machine Learning, J. Ross Quinlan, The MorganKaufmann Series in Machine Learning, Pat Langley, Series Editor, October1992, ISBN 1-55860-238-0). Commercial software for structuring andexecution of decision trees is available (e.g., CART (5), SalfordSystems, San Diego, Calif.; C4.5 (6), RuleQuest Research Pty Ltd., StIves NSW Australia; and Dgraph (1,3), Jon Oliver, Cygnus, Redwood City,Calif.) and may be used in the methods of the present invention in viewof the teachings of the present specification. A simple version of sucha decision tree is to choose a threshold current glucose value reading,a threshold body temperature value, and a threshold skin conductance(sweat) value. If a current (or predicted) glucose value reading isequal to or below the threshold glucose value, then the body temperatureis evaluated. If the body temperature is below the threshold bodytemperature value, then skin conductance is evaluated. If skinconductance is greater than the threshold skin conductance value, then ahypoglycemic event is predicted.

For example, a first level decision is made by the algorithm based onthe most recent glucose value obtained by the monitoring device comparedto initial thresholds that may indicate a hypoglycemic event. Forexample, the algorithm may compare the current blood glucose value(time=n) or a predicted glucose value (time=n+1) to a threshold value(e.g., 100 mg/dL). If the glucose value is greater than the thresholdvalue then a decision is made by the algorithm to continue monitoring.If the glucose level is less than or equal to the threshold glucoselevel then the algorithm continues with the next level of the decisiontree.

The next level of the decision tree may be an evaluation of thesubject's body temperature reading at time (n), which is compared to athreshold body temperature. For example, if the body temperature isgreater than the threshold body temperature (e.g., 33.95° C. ) then adecision is made by the algorithm to continue monitoring. If the bodytemperature is less than or equal to the threshold the threshold bodytemperature (e.g., 33.95° C.) then the algorithm continues with the nextlevel of the decision tree.

The next level of the decision tree may be an evaluation of thesubject's skin conductance reading at time (n), which is compared to athreshold skin conductance. For example, if the skin conductance (i.e.,sweat reading) is less than the threshold skin conductance (e.g., 0.137sweat sensor reading) then a decision is made by the algorithm tocontinue monitoring. If the skin conductance is greater than or equal tothe threshold skin conductance then the algorithm predicts ahypoglycemic event.

The decision tree could be further elaborated by adding further levels.For example, after a determination that a hypoglycemic event is possiblethe next glucose level can be evaluated to see if it is above or belowthe threshold value. Both body temperature and skin conductance could betested as above once again to confirm the prediction of a hypoglycemicevent.

The most important attribute is typically placed at the root of thedecision tree. In one embodiment of the present invention the rootattribute is the current glucose reading. In another embodiment, apredicted glucose reading at a future time point may be the rootattribute. Alternatively, body temperature or skin conductance could beused as the root attribute.

Further, thresholds need not be established a priori. The algorithm canlearn from a database record of an individual subject's glucosereadings, body temperature, and skin conductance. The algorithm cantrain itself to establish threshold values based on the data in thedatabase record using, for example, a decision tree algorithm.

Further, a decision tree may be more complicated than the simplescenario described above. For example, if skin conductance (i.e., sweat)is very high the algorithm may set a first threshold for the bodytemperature which is higher than normal, if the skin conductance readingis medium, the algorithm might set a relatively lower body temperaturethreshold, etc.

By selecting parameters (e.g., current or future glucose reading, bodytemperature, skin conductance) and allowing the algorithm to trainitself based on a database record of these parameters for an individualsubject, the algorithm can evaluate each parameter as independent orcombined predictors of hypoglycemia. Thus, the hypoglycemia predictionmodel is being trained and the algorithm determines what parameters arethe most important indicators. A decision tree may be learnt in anautomated way from data using an algorithm such as a recursivepartitioning algorithm. The recursive partitioning algorithm grows atree by starting with all the training examples in the root node. Theroot node may be “split,” for example, using a three-step process asfollows. (1) The root node may be split on all the attributes available,at all the thresholds available (e.g., in a training database). To eachconsidered split a criteria is applied (such as, GIM index, entropy ofthe data, or message length of the data). (2) An attribute (A) and athreshold (T) are selected which optimize the criteria. This results ina decision tree with one split node and two leaves. (3) Each example inthe training database is associated with one of these two leaves (basedon the measurements of the training example). Each leaf node is thenrecursively split using the three-step process. Splitting is continueduntil a stopping criteria is applied. An example of a stopping criteriais if a node has less than 50 examples from the training database thatare associated with it.

In a further embodiment, at each level of the decision in the decisiontree, the algorithm software can associate a probability with thedecision. The probabilities at each level of decision can be evaluated(e.g., summed) and the cumulative probability can be used to determinewhether to set off an alarm indicating a hypoglycemic event.

Receiver Operating Characteristic (ROC) curve analysis can be applied todecision tree analysis described above ROC analysis is another thresholdoptimization means. It provides a way to determine the optimal truepositive fraction, while minimizing the false positive fraction. A ROCanalysis can be used to compare two classification schemes, anddetermine which scheme is a better overall predictor of the selectedevent (e.g., a hypoglycemic event); for example, a ROC analysis can beused to compare a simple threshold classifier with a decision tree. ROCsoftware packages typically include procedures for the following:correlated, continuously distributed as well as inherently categoricalrating scale data; statistical comparison between two binormal ROCcurves; maximum likelihood estimation of binormal ROC curves from set ofcontinuous as well as categorical data; and analysis of statisticalpower for comparison of ROC curves. Commercial software for structuringand execution of ROC is available (e.g., Analyse It for Microsoft Excel,Analyse-It Software, Ltd., Leeds LS125XA, England, UK; MedCalc®, MedCalcSoftware, Mariakerke, Belgium; AccuROC, Accumetric Corporation,Montreal, Quebec, Calif.).

Related techniques that can be applied to the above analyses include,but are not limited to, Decision Graphs, Decision Rules (also calledRules Induction), Discriminant Analysis (including Stepwise DiscriminantAnalysis), Logistic Regression, Nearest Neighbor Classification, NeuralNetworks, and Naïve Bayes Classifier.

Although preferred embodiments of the subject invention have beendescribed in some detail, it is understood that obvious variations canbe made without departing from the spirit and the scope of the inventionas defined by the appended claims.

1. A method for predicting a hypoglycemic event in a subject, saidmethod comprising, determining (i) a threshold glucose value thatcorresponds to said hypoglycemic event, and (ii) a threshold skinconductance and/or temperature value that is correlated with saidhypoglycemic event, obtaining a series of glucose measurement values atselected time intervals using a method comprising obtaining a raw signalspecifically related to a glucose amount or concentration in the subjectfor a given time interval, correlating the raw signal with a glucosemeasurement value indicative of the amount or concentration of glucosepresent in the subject in said given time interval, repeating saidobtaining and correlating to provide a series of glucose measurementvalues at selected time intervals, predicting a glucose measurementvalue at a further time interval subsequent to said series of glucosemeasurement values, and comparing said predicted glucose measurementvalue to said threshold glucose value, when the predicted glucosemeasurement value is less than or equal to the threshold glucose value ahypoglycemic is predicted; measuring skin conductance and/or temperatureof the subject concurrently simultaneously, or sequentially with saidobtaining of the series of glucose measurement values to obtain a skinconductance and/or temperature value or trend of skin conductance and/ortemperature values, and comparing said skin conductance and/ortemperature value or trend of skin conductance and/or temperature valueswith said threshold skin conductance and/or temperature value todetermine whether said skin conductance and/or temperature value ortrend of skin conductance and/or temperature values indicates ahypoglycemic event; predicting a hypoglycemic event in said subject whenboth (i) comparing the predicted glucose measurement value to saidthreshold glucose value indicates a hypoglycemic event at said furthertime interval and (ii) comparing said skin conductance and/ortemperature value or trend of skin conductance and/or temperature valueswith said threshold skin conductance and/or temperature value indicatesa hypoglycemic event and providing an alert to the subject when ahypoglycemic event is predicted.
 2. The method of claim 1, wherein theselected time intervals are evenly spaced.
 3. The method of claim 1,wherein the series of lucose measurement values comprises three or morediscrete glucose measurement values.
 4. The method of claim 3, whereinthe further time interval occurs one time interval after the series ofglucose measurement values.
 5. The method of claim 1, wherein the valuesor trend of values for both skin conductance readings and temperatureare used to predict the likelihood of a hypoglycemic event.
 6. Themethod of claim 3, wherein said predicting of the glucose measurementvalue at a further time interval is carried out using said series ofthree or more glucose measurement values in a series functionrepresented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$ wherein y is the measurement value of glucose, n is thetime interval between glucose measurement values, and α is a real numberbetween 0 and
 1. 7. The method of claim 6, wherein the series functionis used to predict the value of y_(n+1) wherein time interval n+1 occursone time interval after the series of glucose measurement values isobtained. 8-9. (canceled)
 10. The method of claim 1, wherein samplecomprising glucose is extracted from the subject into one or morecollection reservoirs to obtain an amount or concentration of glucose ina reservoir.
 11. The method of claim 10, wherein the one or morecollection reservoirs are in contact with the skin or mucosal surface ofthe subject and the sample is extracted using an iontophoretic currentapplied to said skin or mucosal surface.
 12. The method of claim 10,wherein at least one collection reservoir comprises an enzyme thatreacts with the extracted glucose to produce an electrochemicallydetectable signal to provide said raw signal.
 13. The method of claim4412 wherein said enzyme is glucose oxidase.
 14. The method of claim 1,wherein said obtaining of the series of glucose measurement values isperformed using a near-IR spectrometer.
 15. A glucose monitoring systemfor measuring glucose in a subject, said system comprising, in operativecombination: a sensing mechanism in operative contact with the subjector with a glucose-containing sample extracted from the subject, whereinsaid sensing mechanism obtains a raw signal specifically related toglucose amount or concentration in the subject; a first device to obtainskin conductance readings or temperature readings from the subject, andone or more microprocessors in operative communication with the sensingmechanism, wherein said microprocessors comprise programming to (i)control the sensing mechanism to obtain a series of raw signals atselected time intervals, (ii) correlate the raw signals with measurementvalues indicative of the amount or concentration of glucose present inthe subject to obtain a series of glucose measurement values, (iii)predict a glucose measurement value at a further time interval,subsequent to obtaining the series of glucose measurement values, (iv)compare said predicted glucose measurement value to a threshold glucosevalue, wherein a predicted glucose measurement value less than or equalto the threshold glucose value is designated to be hypoglycemic, (v)control the first device to measure skin conductance readings ortemperature readings of the subject, (vi) compare said skin conductancereadings or temperature readings with a threshold skin conductance ortemperature value or trend of skin conductance or temperature values todetermine whether said skin conductance readings or temperature readingsindicate a hypoglycemic event, (vii) predict a hypoglycemic event insaid subject when both (a) comparing said predicted glucose measurementvalue to said threshold glucose value indicates a hypoglycemic event atsaid further time intervals, and (b) comparing said skin conductancereadings or temperature readings with a threshold skin conductance ortemperature value or trend of skin conductance or temperature valuesindicates a hypoglycemic event; and (viii) control providing an alert tothe subject when a hypoglycemic event is predicted.
 16. The monitoringsystem of claim 15, wherein the sensing mechanism comprises a biosensorhaving an electrochemical sensing element.
 17. The monitoring system ofclaim 15, wherein the sensing mechanism comprises a near-IRspectrometer.
 18. The monitoring system of claim 15, wherein said firstdevice to obtain said skin conductance readings is a sweat probe. 19.The monitoring system of claim 15, wherein said first device to obtainsaid temperature readings is a temperature probe.
 20. The monitoringsystem of claim 15, wherein the selected time intervals are evenlyspaced.
 21. The monitoring system of claim 15, wherein the series oflucose measurement values obtained comprises three or more discreteglucose measurement values.
 22. The monitoring system of claim 21,wherein the further time interval occurs one time interval after theseries of glucose measurement values.
 23. The monitoring system ofclaim, wherein both skin conductance readings and temperature readingsare used to predict the likelihood of a hypoglycemic event.
 24. Themonitoring system of claim 21, wherein said predicting of glucosemeasurement value at a further time interval is carried out using saidseries of three or more glucose measurement values in a series functionrepresented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$ wherein y is the measurement value of glucose, n is thetime interval between lucose measurement values, and a is a real numberbetween 0 and
 1. 25. The monitoring system of claim 24, wherein theseries function is used to predict the value of y_(n+1) wherein timeinterval n+1 occurs one time interval after the series of glucosemeasurement values is obtained.
 26. One or more microprocessors,comprising programming to: (i) control a sensing mechanism to obtain aseries of raw signals at selected time intervals, wherein each rawsignal is related to an amount or concentration of glucose in a subjects(ii) correlate the raw signals with glucose measurement valuesindicative of the amount or concentration of glucose present in thesubject to obtain a series of glucose measurement values-, (iii) predicta glucose measurement value at a further time interval, subsequent toobtaining the series of glucose measurement values; (iv) compare saidpredicted glucose measurement value to a threshold lucose value, whereina predicted lucose measurement value less than or equal to the thresholdglucose value is designated to be hypoglycemic; (v) control a firstdevice to measure skin conductance readings or temperature readings ofthe subject; (vi) compare said skin conductance readings or temperaturereadings with a threshold skin conductance or temperature value or trendof skin conductance or temperature values to determine whether said skinconductance readings or temperature readings indicate a hypoglycemicevent; and (vii) predict a hypoglycemic event in said subject when both(a) comparing said predicted glucose measurement value to said thresholdglucose value indicates a hypoglycemic event at said further timeinterval, and (b) comparing said skin conductance readings ortemperature readings with a threshold skin conductance or temperaturevalue or trend of skin conductance or temperature values indicates ahypoglycemic event (viii) control providing an alert to the subject whena hypoglycemic event is predicted.
 27. The one or more microprocessorsof claim 26, wherein the sensing mechanism comprises a biosensor havingan electrochemical sensing element.
 28. The one or more microprocessorsof claim 26, wherein the sensing mechanism comprises a near-IRspectrometer.
 29. The one or more microprocessors of claim 26, whereinthe selected time intervals are evenly spaced.
 30. The one or moremicroprocessors of claim 26, wherein the series of glucose measurementvalues obtained comprises three or more discrete glucose measurementvalues.
 31. The one or more microprocessors of claim 26, wherein thefurther time interval occurs one time interval after the series ofglucose measurement values.
 32. The one or more microprocessors of claim36, wherein both skin conductance readings and temperature readings areused to predict the likelihood of a hypoglycemic event.
 33. The one ormore microprocessors of claim 30, wherein the predicting the glucosemeasurement value at a further time interval is carried out using saidseries of three or more glucose measurement values in a series functionrepresented by: $\begin{matrix}{y_{n + 1} = {y_{n} + {\alpha\left( {y_{n} - y_{n - 1}} \right)} + {\frac{\alpha^{2}}{2}\left( {y_{n} - {2y_{n - 1}} + y_{n - 2}} \right)}}} & (7)\end{matrix}$ wherein y is the measurement value of glucose, n is thetime interval between lucose measurement values, and a is a real numberbetween 0 and
 1. 34. The one or more microprocessors of claim 33,wherein the series function is used to predict the value of y_(n+1)wherein time interval n+1 occurs one time interval after the series ofglucose measurement values is obtained.
 35. The monitoring system ofclaim 15, further comprising a second device and wherein said firstdevice comprises a sweat probe to obtain said skin conductance readingsand said second device comprises a temperature probe to obtain saidtemperature readings.
 36. The one or more microprocessors of claim 26,further comprising programming to control a second device, wherein saidfirst device provides skin conductance readings and said second deviceprovides temperature readings.